Abstract Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …

Rolling bearing fault detection is critical for improving production efficiency and lowering
accident rates in complicated mechanical systems, as well as huge monitoring data, posing …

Deep learning (DL)-based methods have advanced the field of Prognostics and Health
Management (PHM) in recent years, because of their powerful feature representation ability …

Convolutional neural networks (CNNs) are being utilized for mechanical fault diagnosis, due
to its excellent automatic discriminative feature learning ability. However, the poor …

Digital twin (DT) is emerging as a key technology for smart manufacturing. The high fidelity
DT model of the physical assets can produce system performance data that is close to …

Accurate prediction of remaining useful life (RUL) has been a critical and challenging
problem in the field of prognostics and health management (PHM), which aims to make …

With the rapid development of manufacturing industry, machine fault diagnosis has become
increasingly significant to ensure safe equipment operation and production. Consequently …

Diagnosis of mechanical faults in manufacturing systems is critical for ensuring safety and
saving costs. With the development of data transmission and sensor technologies …

Abstract Fault Detection and Diagnosis (FDD) of rotating machinery plays a key role in
reducing the maintenance costs of the manufacturing systems. How to improve the FDD …

Accurate fault diagnosis is critical to ensure the safe and reliable operation of rotating
machinery. Data-driven fault diagnosis techniques based on Deep Learning (DL) have …